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1

Performance comparison of wavelet thresholding techniques on weak ECG signal denoising

Üstündağ M., Şengür A., Gökbulut M., Ata F.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 5

63-66

EN

The Electrocardiogram (ECG) signal is a biological non-stationary signal which contains important information about rhythms of heart. ECG signals can be buried by various types of noise. These types can be electrode movement, strong electromagnetic effect and muscle noise. Noisy ECG signal has been denoised using signal processing. This paper presents a weak ECG signal denoising method based on intervaldependent thresholds of wavelet analysis. Several experiments were conducted to show the effectiveness of the interval-dependent thresholding method and compared the results with the soft and hard wavelet thresholding methods for denoising. The results are evaluated by calculating the root mean square error and the correlation coefficient.

PL

W artykule przedstawiono metodę odszumiania sygnałów elektrokardiografu w oparciu o analizę falkową. W rozwiązaniu zastosowano progowanie przedziałowo-zależne. Na podstawie poczynionych eksperymentów oraz wyznaczonych wartości RMS błędu i współczynnika korelacji wykazano jego skuteczność. Dodatkowo dokonano porównania otrzymanych wyników z działaniem metod miękkiego i twardego progowania falkowego.

2

The exploitation of wavelet de-noising to detect bearing faults

Al-Raheem K. F., ROY A., Ramachandran K. P., Harrison D. K., Grainger S.

Journal of KONBiN

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2007

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No. 1 (3)

7-16

EN

Failure diagnosis is an important component of the Condition Based Maintenance (CBM) activities for most engineering systems. Rolling element bearings are the most common cause of rotating machinery failure. The existence of the amplitude modulation and noises in the faulty bearing vibration signal present challenges to effective fault detection method. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, we proposed new approach for bearing fault detection based on the autocorrelation of wavelet de-noised vibration signal through a wavelet base function derived from the bearing impulse response. To improve the fault detection process the wavelet parameters (damping factor and center frequency) are optimized using maximization kurtosis criteria to produce wavelet base function with high similarity with the impulses generated by bearing defects, that leads to increase the magnitude of the wavelet coefficients related to the fault impulses and enhance the fault detection process. The results show the effectiveness of the proposed technique to reveal the bearing fault impulses and its periodicity for both simulated and real rolling bearing vibration signals.

3

Non-regular distortions in ECG signal introduced by wavelet denoising

Romaniuk P.

Journal of Medical Informatics & Technologies

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2000

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Vol. 1

BI 15--21

EN

This paper addresses sharp, non-regular distortions appearing in ECG signal after applying wavelet denoising. Introduced distortions cause loss of the signal smoothness, that in the case of ECG signal are unacceptable. Source of such distortions was investigated and explained. Results of signal denoising process that is based on two wavelets, chosen from one family, are described and compared.